class_docstr: Mixin class for all classifiers in scikit-learn.
docstr: Return the mean accuracy on the given test data and labels.
  
  In multi-label classification, this is the subset accuracy
  which is a harsh metric since you require for each sample that
  each label set be correctly predicted.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      Test samples.
  
  y : array-like of shape (n_samples,) or (n_samples, n_outputs)
      True labels for X.
  
  sample_weight : array-like of shape (n_samples,), default=None
      Sample weights.
  
  Returns
  -------
  score : Tensor
      Mean accuracy of self.predict(X) wrt. y.
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code: def score(
      self, X, y, sample_weight=None, session=None, run_kwargs=None
  ):
      """
      Return the mean accuracy on the given test data and labels.
  
      In multi-label classification, this is the subset accuracy
      which is a harsh metric since you require for each sample that
      each label set be correctly predicted.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Test samples.
  
      y : array-like of shape (n_samples,) or (n_samples, n_outputs)
          True labels for X.
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights.
  
      Returns
      -------
      score : Tensor
          Mean accuracy of self.predict(X) wrt. y.
      """
      from .metrics import accuracy_score
  
      result = accuracy_score(
          y,
          self.predict(X),
          sample_weight=sample_weight,
          session=session,
          run_kwargs=run_kwargs,
      )
      return result
  
file_path: mars/mars/learn/base.py
imports: from sklearn.base import BaseEstimator
  from .utils.validation import check_array, check_X_y
label_desc: Use image node_id 1 for calling the ClassifierMixin obj's underlying member method code with example usage: obj.score(X, y, sample_weight, session, run_kwargs) and returns: result